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Critically, pathogen intelligence allows for genomic epidemiology. Various phylogenetic analyses can provide statistical evidence of the relatedness of isolates, which in turn allows for accurate cluster or outbreak detection. Even more impactful, genomic surveillance can identify outbreaks sooner, even from the first cases (2)—well before a cluster is identified by “astute clinicians” or from the tabulation of case report data, which are still the gold standards in healthcare and public health, respectively. Additionally, such intel will allow for proper inclusion and exclusion assignment of suspect cluster cases. Both aspects are critical to the detection and ascertainment of the scope of outbreaks, while the latter is also an imperative for empirically proving that suspect clusters may be truly unrelated infections (3). Therefore, the use of genomic-based surveillance for early outbreak detection and more accurate outbreak case inclusion promises to significantly reduce healthcare costs, reduce morbidity and mortality, and improve public health efficiency by focusing investigations on only true genomically linked cases. Pathogen intelligence gathering allows for “precision epidemiology” to finally take its place alongside precision medicine.

In addition to helping infection prevention and control departments detect outbreaks, a pathogen intelligence approach could provide actionable knowledge on the clinical features of the microbe, including antimicrobial resistance markers and potential virulence genes, as Struelens et al. highlight (1). While detection of resistance mutations and/or genes does not always capture a complete susceptibility profile for a given pathogen, current reference sequence databases do contain the vast majority of known resistance mechanisms (4). Of course, the mere presence of such targets does not equate to active resistance (e.g., some targets are constitutive housekeeping or efflux genes, and their presence alone reveals little about their resistance phenotype). The inclusion of transcript (i.e., mRNA) analyses can identify if such target genes are being modulated for resistance effect. Amplicon or targeted sequencing-based approaches allow for this intel to be gathered directly from clinical samples without the need for culture-based phenotyping or whole genome sequencing of isolates. The sequencing resolution provided by advanced techniques even allows for early pre-phenotypic resistance to be detected in time to potentially prevent actual onset resistance (5). Improved techniques for genome capture, target enrichment, host subtraction, and sequencing are helping to make clinical whole genome and even metagenome sequencing straight from clinical samples a reality (6) and could provide even more clinical intel on infections in the future.

As was made abundantly clear during the pandemic, the collective world of healthcare and public health needs better intel on microbial threats as they emerge and develop. Pathogen intel was notably used 15 years ago during the millennium’s first true pandemic, the 2009 H1N1pdm09 “swine flu” pandemic. A handful of laboratories provided the critical sequencing data to track the origin and movement of the novel influenza strain as it moved out of Mexico and spread globally (7). Ten years later, thousands of labs around the world joined forces to conduct the largest sequencing effort in human history, tracking the macro- and micro-evolution of the SARS-CoV-2 virus. This moonshot effort provided a near real-time intelligence system for state and national policymakers and health officials to adjust response strategies to the changing variants and their critical phenotype-altering mutations. The world watched as mutations in the shape-shifting spike protein resulted in the virus’ immune escape, therapy resistance, and cell invasion strategies and capabilities, ultimately resulting in the pandemic-ending Omicron variant and its endless subvariants. However, despite the enormity and intensity of the sequencing and analysis efforts, the gaps in linkages between surveillance systems, disunity of local, national, and global strategies, and variable coverage across global populations need to be improved before the next pandemic (1).

A pathogen intelligence approach could also provide expansive information on the biology, ecology, and evolutionary history of the microbe as a biological, rather than etiological, agent. Beyond focusing on the immediate intel for patient or population health, we can gather intel on the pathogen as a microbe, and how it lives and thrives in its environment. This is critical, as the clinical/epidemiological anthropomorphic view is a highly distorted perspective, ignoring that most pathogens are just microbes with some pathogenic potential and often the intel needed to respond to or mitigate a health threat is in the biology or ecology of the microbe. For example, it is now thought that Cryptococcus neoformans is an accidental animal fungal pathogen that has formed intracellular defenses through its natural survival strategies to endure amoeba engulfment in its natural habitat (8). Genomic analysis can identify which genetic features drive this natural survival mechanism, which may in turn contribute to future medical countermeasure strategies. With genomics, we can place a pathogenic microbe in the phylogenetic context of its relevant taxonomical relationships to understand what has been lost or gained genetically to create pathogenic descendants from a non-pathogenic family history. For example, genomics could demonstrate that another pathogenic fungal genus, Coccidioides, the etiologic agent of Valley fever, has both lost the plant protease genes of its relatives and gained animal protease genes, radically transforming it into a mammalian pathogen (9). Phylogenetics also holds the key to understanding where, when, and how newly identified pathogens emerge. Sticking with Coccidioides, this fungus, predominantly found in the arid thermic soils of the American southwest, was recently found both in soils and patients in faraway south-central Washington (WA). Genomic analyses have shown that this is a clonal population in WA, and is likely hundreds to thousands of years old, helping mycologists and public health scientists to understand its relative importance in the natural history of the fungus and the epidemiology of the disease today (10). Given the close tie to animals and the environment for many microbes that become pathogens, a One Health approach to biological intel gathering is a necessity. Metagenomics and genomic clock analyses provide intel to help understand the One Health origins of everything: from HIV and Mycobacterium tuberculosis to Candida auris. Such biological intel on microbial family secrets may hold the key to not only better treatment of current infections but perhaps also identification and prevention of would-be pathogens before they emerge.